Abstract:

In order to improve the accuracy of the nonlinear sequential fusion algorithm, a new multi-sensor sequential fusion algorithm based on square-root cubature Kalman filter (SRCKF) is proposed. The proposed algorithm uses the third degree spherical-radial cubature rule to calculate the mean and covariance of the nonlinear process, and hence, overcomes the shortcomings of low performance in extended Kalman filter and complex parameters in square-root unscented Kalman filter. Meanwhile, the square-root covariance matrix replaces the covariance matrix in filtering recursion. In this way, the numerical stability of the algorithm is guaranteed and the tracking accuracy is improved. The performance of the proposed algorithm is tested by the reentry trajectory tracking model with known ballistic coefficients. Simulation results show that the proposed algorithm has good tracking performance, and is therefore an effective nonlinear sequential fusion tracking algorithm.